Browse wiki

Jump to: navigation, search
On the influence propagation of web videos
Abstract We propose a novel approach to analyze howWe propose a novel approach to analyze how a popular video is propagated in the cyberspace, to identify if it originated from a certain sharing-site, and to identify how it reached the current popularity in its propagation. In addition, we also estimate their influences across different websites outside the major hosting website. Web video is gaining significance due to its rich and eye-ball grabbing content. This phenomenon is evidently amplified and accelerated by the advance of Web 2.0. When a video receives some degree of popularity, it tends to appear on various websites including not only video-sharing websites but also news websites, social networks or even Wikipedia. Numerous video-sharing websites have hosted videos that reached a phenomenal level of visibility and popularity in the entire cyberspace. As a result, it is becoming more difficult to determine how the propagation took place-was the video a piece of original work that was intentionally uploaded to its major hosting site by the authors, or did the video originate from some small site then reached the sharing site after already getting a good level of popularity, or did it originate from other places in the cyberspace but the sharing site made it popular. Existing study regarding this flow of influence is lacking. Literature that discuss the problem of estimating a video's influence in the whole cyberspace also remains rare. In this article we introduce a novel framework to identify the propagation of popular videos from its major hosting site's perspective, and to estimate its influence. We define a Unified Virtual Community Space (UVCS) to model the propagation and influence of a video, and devise a novel learning method called Noise-reductive Local-and-Global Learning (NLGL) to effectively estimate a video's origin and influence. Without losing generality, we conduct experiments on annotated dataset collected from a major video sharing site to evaluate the effectiveness of the framework. Surrounding the collected videos and their ranks, some interesting discussions regarding the propagation and influence of videos as well as user behavior are also presented. well as user behavior are also presented.
Abstractsub We propose a novel approach to analyze howWe propose a novel approach to analyze how a popular video is propagated in the cyberspace, to identify if it originated from a certain sharing-site, and to identify how it reached the current popularity in its propagation. In addition, we also estimate their influences across different websites outside the major hosting website. Web video is gaining significance due to its rich and eye-ball grabbing content. This phenomenon is evidently amplified and accelerated by the advance of Web 2.0. When a video receives some degree of popularity, it tends to appear on various websites including not only video-sharing websites but also news websites, social networks or even Wikipedia. Numerous video-sharing websites have hosted videos that reached a phenomenal level of visibility and popularity in the entire cyberspace. As a result, it is becoming more difficult to determine how the propagation took place-was the video a piece of original work that was intentionally uploaded to its major hosting site by the authors, or did the video originate from some small site then reached the sharing site after already getting a good level of popularity, or did it originate from other places in the cyberspace but the sharing site made it popular. Existing study regarding this flow of influence is lacking. Literature that discuss the problem of estimating a video's influence in the whole cyberspace also remains rare. In this article we introduce a novel framework to identify the propagation of popular videos from its major hosting site's perspective, and to estimate its influence. We define a Unified Virtual Community Space (UVCS) to model the propagation and influence of a video, and devise a novel learning method called Noise-reductive Local-and-Global Learning (NLGL) to effectively estimate a video's origin and influence. Without losing generality, we conduct experiments on annotated dataset collected from a major video sharing site to evaluate the effectiveness of the framework. Surrounding the collected videos and their ranks, some interesting discussions regarding the propagation and influence of videos as well as user behavior are also presented. well as user behavior are also presented.
Bibtextype article  +
Doi 10.1109/TKDE.2013.142  +
Has author Liu J. + , Yang Y. + , Huang Z. + , Shen H.T. +
Has extra keyword Behavioral research + , Computers + , Data processing + , Multimedia systems + , Virtual reality + , Cyberspaces + , Learning methods + , News websites + , User behaviors + , Video sharing + , Virtual community + , Web video + , Wikipedia + , Websites +
Has keyword Unified virtual community space + , Video influence estimation + , Video origin estimation +
Issn 10414347  +
Issue 8  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 1961–1973  +
Published in IEEE Transactions on Knowledge and Data Engineering +
Title On the influence propagation of web videos +
Type journal article  +
Volume 26  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 15:09:34  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 15:09:34  +
DateThis property is a special property in this wiki. 2014  +
hide properties that link here 
On the influence propagation of web videos + Title
 

 

Enter the name of the page to start browsing from.